2.3 Institutional Innovation, Data Governance and Economic Coordination

The literature on institutional innovation within innovation systems has long emphasized the role of governance reform, regulatory redesign, and policy experimentation in shaping technological trajectories. Institutional change is frequently framed as the reconfiguration of formal rules and coordination structures that enable sustainability transitions and systemic transformation (Solis-Navarrete et al., 2023). In the context of decarbonization and green growth, institutional innovation is associated with the creation of regulatory frameworks and financial mechanisms that steer investment toward carbon neutrality and technological upgrading (Pan & Jiang, 2025; An & Di, 2024). These studies highlight that innovation systems are embedded in governance architectures that structure incentives, allocate authority, and shape actor interactions.
Parallel to this strand, a growing body of research examines data governance as a key enabler of digital innovation. Data governance is typically conceptualized as a managerial and organizational capability that enhances data quality, privacy compliance, stakeholder engagement, and AI system performance (Weinbaum & Kamp, 2026; Liu et al., 2026). Public data openness and the marketization of data elements are analysed as drivers of firm growth and innovation outcomes (Gao et al., 2026). Within this perspective, data governance improves innovation performance by facilitating digital transformation and reducing operational uncertainty.
However, these literatures—while adjacent—remain largely disconnected at the level of economic theory. Institutional innovation studies rarely examine how governance architectures restructure microeconomic coordination mechanisms. Conversely, data governance research often treats governance as an internal managerial process rather than as a systemic institutional arrangement shaping economic exchange. Notably, until recently, little work explicitly linked data governance to foundational concepts in transaction cost economics or information asymmetry theory.

Emerging research begins to address this gap. Recent studies show that data governance mechanisms influence strategic behaviour under conditions of asymmetric information. Transparency-enhancing governance structures can mitigate moral hazard, reduce opportunistic data misuse, and improve trust in digital ecosystems (Kim et al., 2026). Privacy regulation reforms, such as those following China’s Personal Information Protection Law, are analysed as institutional interventions that alter incentives for data disclosure and strategic behaviour (Chen et al., 2026). Evolutionary game models demonstrate that governance rule design affects equilibrium outcomes in healthcare data sharing systems characterized by hidden action and risk externalities (Yao & Liu, 2025). Similarly, research on digital platforms and circular economy ecosystems suggests that governance arrangements shape data disclosure strategies and influence cooperation under uncertainty (Cai & Yang, 2025).
These contributions indicate that data governance does interact with moral hazard, risk exposure, and information asymmetry. Yet three limitations persist. First, governance is typically treated as a sector-specific regulatory or compliance mechanism rather than as an institutional innovation reshaping broader economic coordination structures. Second, the implications of data governance for transaction cost restructuring—search costs, verification costs, monitoring intensity, and enforcement mechanisms—are rarely formalized in economic terms. Third, the connection between data governance and innovation system dynamics, including market formation and capability upgrading, remains underdeveloped.
From a transaction cost perspective, institutions exist to reduce uncertainty and facilitate exchange under incomplete information. Markets fail when actors cannot verify claims, monitor effort, or enforce agreements at reasonable cost. Classic analyses of supply chain innovation under asymmetric information show that hidden information and hidden action distort investment incentives and generate underinvestment or strategic misalignment (Ni et al., 2021; Zhou et al., 2012). Monitoring mechanisms such as audits can mitigate these distortions but remain costly and imperfect (Nikoofal & Gümüş, 2020). Governance architectures therefore determine the feasibility and efficiency of innovation coordination.
Data governance structures perform precisely these functions. By defining standards for data generation, interoperability protocols, access rights, and verification procedures, data governance transforms the informational environment within which economic actors operate. It determines whether attributes remain private credence characteristics or become standardized and verifiable signals. In doing so, data governance directly influences adverse selection, moral hazard, transaction costs, information asymmetry, and contract design. Standardized lifecycle data, like the one in DPPs, reduce information rents, lower due diligence costs, and enable more precise allocation of risk.
This reconceptualization elevates data governance from a managerial digital capability to a foundational institutional infrastructure. Rather than merely improving internal performance metrics, data governance restructures the information architecture of markets. It affects how actors search for partners, negotiate contracts, monitor compliance, and allocate investment. In innovation systems characterized by multi-actor interdependence—particularly sustainability transitions and CE ecosystems—these effects become systemic.
Sustainability attributes such as carbon intensity, environmental performance, recyclability, durability, and reparability are typically credence characteristics that suffer from acute information asymmetry. Institutional innovation in sustainability has focused on policy mandates and financial instruments (Pan & Jiang, 2025; An & Di, 2024), but has paid comparatively less attention to the role of standardized data infrastructures in reducing coordination costs. Without credible data governance mechanisms, sustainability claims remain difficult to verify, exposing markets to greenwashing and strategic misrepresentation. Under such conditions, adverse selection may deter investment in genuinely sustainable innovation.
DPPs can be interpreted as a sectoral instantiation of data governance as institutional innovation. Rather than functioning solely as compliance registries, DPPs embed standardized lifecycle data within a regulatory and technical governance architecture. By defining interoperability standards, data disclosure rules, and verification protocols, DPP infrastructures reshape the informational foundations of circular markets. They reduce asymmetric information across suppliers, producers, users, repairers, remanufacturers, refurbishers, recyclers, financiers, and regulators, thereby lowering transaction costs and information asymmetries, and enabling new contractual arrangements.
Thus, while recent literature acknowledges that data governance influences strategic behaviour under asymmetric information, it has not yet fully integrated these insights into innovation economics and market formation theory. Conceptualizing data governance and DPPs as institutional innovations that restructure transaction costs and information asymmetries, provides a missing bridge between digital governance studies and economic coordination theory. The following section develops this bridge by identifying the mechanisms through which DPP-based data governance may reshape innovation incentives and enable circular market formation.
In innovation systems characterized by high lifecycle complexity and credence attributes, institutionalized data governance infrastructures—such as DPPs—reduce information asymmetries and transaction costs by transforming private sustainability attributes into standardized and verifiable signals. By restructuring the informational foundations of exchange, such infrastructures enhance incentive compatibility, lower coordination costs, and create the institutional conditions necessary for circular market formation and business model innovation. The following section decomposes this overarching proposition into four interrelated mechanisms through which DPP infrastructures reshape innovation dynamics.